Automatic Paddy Leaf Disease Detection Based on GLCM Using Multiclass Support Vector Machine

  • Venuja Satgunalingam Vavuniya Campus of the University of Jaffna, P.O.Box 43000, Srilanka
  • Rajeetha Thaneeshan Vavuniya Campus of the University of Jaffna, P.O.Box 43000, Srilanka
Keywords: Paddy Blast, Brown Spot, Thresholding, Support Vector Machine


The paddy leaf diseases have increased rapidly in the recent years because of globalization, environmental pollution and climate changes which reduce the production of rice and economy of the country. For healthy growth of rice plants there is a need of automatic system which can detect the paddy diseases automatically on time to give the proper treatment for the affected plants. In this paper, we proposed a methodology to develop an automatic system for detect the paddy disease which are Paddy Blast Disease, Brown Spot Disease, Narrow Brown Spot Disease using MATLAB. This paper concentrate on the image processing techniques used to enhance the quality of the image and Multiclass Support Vector Machine to classify the paddy diseases. The methodology involves image acquisition, pre-processing, segmentation, feature extraction and classification of the paddy diseases. Image segmentation technique is used to detect infected parts of leaf by using canny edge detection, multilevel thresholding and region growing techniques. We extract texture features using GLCM (grey level co- occurrence matrix) techniques, additionally we extract color and shape features to improve the accuracy of the framework   and use Multiclass Support Vector Machine for classification. We achieved 87.5% accuracy for the test dataset. 


. S. Weizheng, W. Yachun, C. Zhanliang, and W. Hongda, “Grading methodof leaf spot disease based on image processing,” in Computer Science andSoftware Engineering, 2008 International Conference on, vol. 6. IEEE,2008, pp. 491–494.

. D. Al Bashish, M. Braik, and S. Bani-Ahmad, “A framework for detection and classification of plant leaf and stem diseases,” in Signal and Image Processing (ICSIP), 2010 International Conference on. IEEE, 2010, pp. 113–118.

. J. P. Shah, H. B. Prajapati, and V. K. Dabhi, “A survey on detection and classification of rice plant diseases,” in 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), March 2016, pp. 1– 8.

. B. S. Prajapati, V. K. Dabhi, and H. B. Prajapati, “A survey on detection and classification of cotton leaf diseases,” in International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016, March 2016, pp. 1–8.

. Q. Yao, Z. Guan, Y. Zhou, J. Tang, Y. Hu, and B. Yang, “Application of support vector machine for detecting rice diseases using shape and color texture features,” in Engineering Computation, 2009. ICEC’09. International Conference on. IEEE, 2009, pp. 79–83.

. G. Anthonys and N. Wickramarachchi, “An image recognition system for crop disease identification of paddy fields in sri lanka,” in 2009 International Conference on Industrial and Information Systems (ICIIS). IEEE, 2009, pp. 403–407.

. R. A. D. Pugoy and V. Y. Mariano, “Automated rice leaf disease detection using color image analysis,” in 3rd international conference on digital image processing. International Society for Optics and Photonics, 2011, pp. 80 090F–80 090F.

. C. Charliepaul, “Classification of rice plant leaf using feature matching,” International Journal On Engineering Technology and Sciences, vol. 1, no. 2, pp. 290–295, nov 2014.

. C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE transactions on image processing, vol. 19, no. 12, pp. 3243–3254, 2010.

. S. T and D. T, “Classification of paddy leaf diseases using shape and color features,” International Journal of Electrical and Electronics Engineers, vol. 7, no. 1, pp. 239–250, 2015.

. A. K. Singh, R. A, and B. S. Raja, “Classification of rice disease using digital image processing and svm,” International Journal of Electrical and Electronics Engineers, vol. 7, no. 1, pp. 294–299, 2015.

. S. Phadikar, J. Sil, and A. Das, “Classification of rice leaf diseases based onmorphological changes,” International Journal of Information and Electronics Engineering, vol. 2, no. 3, p. 460, 2012.

. M. A. A. KAHAR, S. MUTALIB, and S. ABDUL-RAHMAN, “Recent advances in mathematical and computational methods,” in Recent Advances in Mathematical and Computational Methods, pp. 248–257.

. J. W. Orillo, J. D. Cruz, L. Agapito, P. J. Satimbre, and I. Valenzuela, “Identification of diseases in rice plant (oryza sativa) using back propagation artificial neural network,” in Humanoid, Nanotechnology,

. Technology, Communication and Control, Environment and Management (HNICEM), 2014 International Conference on. IEEE, 2014, pp. 1–6.

. L. Liu and G. Zhou, “Extraction of the rice leaf disease image based on bp neural network,” in Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on. IEEE, 2009, pp. 1–3.

. N. N. Kurniawati, S. N. H. S. Abdullah, S. Abdullah, and S. Abdullah, “Investigation on image processing techniques for diagnosing paddy diseases,” in Soft Computing and Pattern Recognition, 2009. SOCPAR’09. International Conference of. IEEE, 2009, pp. 272–277

. K. Majid, Y. Herdiyeni, and A. Rauf, “I-pedia: Mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network,” in Advanced Computer Science and Information Systems (ICACSIS), 2013 International Conference on. IEEE, 2013, pp. 403–406.

. S. Phadikar, J. Sil, and A. K. Das, “Rice diseases classification using feature selection and rule generation techniques,” Computers and electronics in agriculture, vol. 90, pp. 76–85, 2013.

. S. Phadikar and J. Sil, “Rice disease identification using pattern recognition techniques,” in Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on. IEEE, 2008, pp. 420–423.

. G. Maharjan, T. Takahashi, and S. Zhang, “Classification methods based on pattern discrimination models for web-based diagnosis of rice diseases,” Journal of Agricultural Science and Technology, vol. 1, no. 1, pp. 48–56, 2011.

How to Cite
Satgunalingam, V., & Thaneeshan, R. (2020). Automatic Paddy Leaf Disease Detection Based on GLCM Using Multiclass Support Vector Machine. International Journal of Computer (IJC), 39(1), 97-106. Retrieved from